TCM: An efficient lightweight MLP-based network with affine transformation for long-term time series forecasting

Time series forecasting (TSF) involves extracting underlying patterns from past information to predict future sequences over a specific period. Extending the prediction length of time series and improving the prediction accuracy have always been challenging tasks. Autoregressive prediction methods based on Markov chains tend to accumulate errors over time. Although Transformer-based methods with various self-attention mechanisms have shown some improvements, they require higher memory and computational resources. In this work, we present an effective MLP-based TSF framework named TCM, which models the sequence and channel dependencies separately using Token MLP and Channel MLP. Additionally, we employ the Affine Transformation to replace layer normalization or batch normalization, leading to substantial enhancements in both accuracy and inference speed. Compared to current state-of-the-art long-term time series forecasting models, TCM achieves 6.0% relative improvement on seven real-world datasets, including electricity, weather, and illness domains. The TCM model, characterized by its efficiency and lightweight architecture, also makes it suitable for scenarios with high real-time requirements.